Remote job
GCP DevOps Engineer
Job details
About this role
Role overview A senior platform role responsible for owning a production Google Cloud footprint that includes Cloud Run, GKE, Pub/Sub, and BigQuery, with a heavy emphasis on infrastructure as code rather than console-driven changes. The position blends platform engineering, observability, cost management, and incident leadership to give application teams a reliable, automated foundation. It suits an engineer who treats cloud spend, security posture, and deployment reversibility as first-class product decisions.
Responsibilities
- Design and maintain Google Cloud infrastructure in Terraform, covering projects, IAM, networking, Cloud Run, GKE, Pub/Sub, Cloud SQL, and BigQuery. - Build reproducible deployment pipelines with genuine rollback, treating console-only changes as defects to be eliminated. - Enforce least-privilege access across service accounts and workload identity, and centralize secrets in Secret Manager instead of environment files. - Instrument services and infrastructure with Cloud Monitoring, logging, and tracing so incidents begin with concrete data rather than guesswork. - Define SLOs tied to user-facing behavior, configure alerts against those SLOs, and run ongoing cost analysis to right-size the footprint. - Provide paved paths for application teams, lead incident response, write blameless postmortems, and manage multi-environment, multi-tenant setups where isolation is contractually required.
Requirements
- Four or more years of experience running production infrastructure on Google Cloud. - Strong Terraform skills, including authoring reusable modules and understanding state management. - Production Kubernetes experience covering workloads, networking, autoscaling, and cluster debugging. - Scripting depth in Python, Go, or Bash, and confidence with CI systems such as GitHub Actions or Cloud Build.
Nice to have
- Multi-cloud exposure, particularly AWS alongside GCP. - Experience serving GPU or inference workloads on GKE or Cloud Run. - Background in data platform work, including Dataflow, BigQuery modeling, or streaming pipelines.